Objective Analysis of Michigan State and the Big Ten Conference

I will be posting hitting, pitching, and defensive updates for each team in the Big Ten to recap their 2012 seasons with the proper scrutiny. Those are linked at the 2012 Season Recaps tab at the top right. I have no planned schedule on when I will have them posted, but the order is going to be alphabetical. Their timing will likely be sporadic, however.

The Fighting Illini finished the 2012 campaign three games over .500 at 28-25. Boyd Nation currently has Illinois ranked 96th overall. Despite this, the Illini finished just 11-13 in conference play, in a tie for sixth place which left them on the outside of the Big Ten Tournament invitees. This is a recap of their hitting as both a team and individuals.

As a Team

On the whole, the Illini were middle-of-the-road offensively. Their 309 runs on the season were the sixth most in the conference. Illinois posted a 0.326 Weighted On-Base Average (wOBA; refer to this post for more links on what wOBA is) which was a shade over the 0.320 conference average. That gives Illinois seven runs above average offensively which was actually the fourth-best mark in the Big Ten.

I’ve neglected this blog over over a full calendar year. With the NCAA Super Regional’s currently on television, now is as good a time as any to do my first Big Ten Conference data dump/season recaps in over a year.

I’ve tweaked my data (again) to what I feel comfortable with. If you’d like to get a bit nerdy, continue reading down after this paragraph. If you don’t, just know that I’m using the following measures for offense, defense and pitching.

Offense – I’m using Weighted On-Base Average, or wOBA. It uses linear weights to give each type of outcome from a plate appearance a run value. You multiply these values by the number of times each outcome occurred and divide by plate appearances. This will give you a team’s (or player’s) wOBA. The nice thing about wOBA is the ability to easily convert it into a runs above average metric dubbed Weighted Runs Above Average or wRAA. Additionally, you can take it a step further and calculate Weighted Runs Created which is adjusted for league average — or wRC+. This number is the same scale as OPS+ where 100 is league average and each point above/below 100 is equivalent to one percentage point. Why do this? Because wRAA is a counting stat, wRC+ is not. This allows us to compare how well a player performed offensively in his 100 plate appearances to a player who had 180. While the wRAA may favor the latter player, wRC+ will help us decide who actually was better. It takes playing time essentially out of the equation.

Defense – I haven’t changed my calculations on defense at all since my last postings on this space. I am using Defensive Efficiency Ratio because it’s the best team-defense metric at the Major League level. Given how little data we have for the Big Ten compared to the MLB, Defensive Efficiency Ratio is almost undoubtedly the best defensive metric available for the Big Ten. I calculate it just as Baseball Prospectus does and put it on the “plus” scale, where 100 is league average. This allows us to compare across various seasons.

Pitching – I changed my metric over to Fielding Independent Pitching, or FIP. From here, we can calculate a runs above average number for the individual pitcher or team in question quite easily. It attempts to remove defense and focus on things a pitcher can control. However, I haven’t studied whether FIP is a reliable number in college baseball. I’m pretty certain that the theories behind defensive-independent run estimators like FIP hold true at the college level, it’s just not entirely certain. From here, I’ve calculated an Expected FIP (xFIP) of sorts. Instead of normalizing home-run-to-fly-ball-ratio like Fangraphs does, I’ve substituted a league-average home-run-per-contacted-ball percentage for each pitcher. I don’t know how much I’ll present an xFIP data because the lower you move in the baseball ranks, the more a pitcher’s abilities to prevent home runs and induce weak contact increases. It’s a little cross-checker of mine to help me contextualize the data a bit more.

The nerdy part: I’ve calculated custom linear weights by using Tom Tango’s Markov Calculator. For FIP, I calculated custom FIP weightings based on this blog post by Tango called Deconstructing FIP.

Well, day one is in the books on the Big Ten Tournament and Ohio State and Purdue picked up victories. Ohio State had an 8th inning rally which accounted for all five of their runs to beat Minnesota 5-3. Purdue was down 2-0 before firing off seven unanswered runs which, behind the right arm of Matt Morgan was plenty to cruise passed Penn State. You can find plenty of recaps around the web, so I won’t spend too much time on that.

Let’s look ahead to tomorrow where Ohio State will take on the top seed Illinois Fighting illini. Meanwhile, The Boilermakers win got them a match up with the second-seeded Michigan State Spartans. As a result of their losses, fifth-seeded Minnesota will play sixth-seeded Penn State, as well.

I just put together some statistical leaderboards for the 2011 campaign so far. You can find these easily under the 2011 Leaderboard tab on the top right and they will be updated at my discretion (read: when I have time). In an ideal world, I’ll have these updated every Monday or Tuesday, but I make no promises on that.

As of right now, I’ve compiled the team Defensive Efficiency Ratio and data for individual hitters, starting pitchers and relief pitchers. If you’re looking for regular earned run average or a hitters batting average, or on-base percentage, then these leaderboards are not for you.

What I’ve presented, I’ll quickly run through.

Hitters

What I’ve included is the percentage of plate appearances the hitter either strikes out, walks/gets hit by a pitch. Along with that, I’ve included a hitter’s isolated power which I have adjusted using these park factors from Boyd’s World. The data, unfortunately, is not adjusted for competition. As of right now, I’d have to calculate the strength of schedule adjustment on my own and, honestly, I’m not willing to really mess with that for the time being. I also have a Runs Above Average total for each hitter listed that is calculated using the Base Runs method.

Pitchers

I’ve broken pitchers into two leaderboards and three separate roles. For pitchers who start a game in 70% or more of their appearances, they are labeled as starting pitchers. For those who get a start in 40-69% of their appearances, I’ve dubbed them swingmen. Finally, those who start games in 39% of their appearances or fewer, they are relievers.

The stats I have for all pitchers are K% and BB%, like I do for hitters. These are the most important numbers to look at. Sure, I’ve got advanced data with an ERA replicate born out of the Base Runs method and then adjusted for park — from which I get the runs above average totals — but the meat of the data for pitchers is in the strikeout and walk/HBP totals.

Team Levels

I currently have Defensive Efficiency Ratio which is calculated just like it is at Baseball Prospectus. This is the best measure for defense at the major league level. I feel that this holds true — perhaps even more so — at the college level, as well.

For team hitting, I’ve included the teams K%, BB%, BABIP and park adjusted isolated power. Pitching, I have included K%, BB% and BABIP, but also give the teams’ traditional ERA as well as a base runs derived runs allowed metric.

I have a standings page as well. Here, you’ll find actual runs scored and allowed, estimated runs scored and allowed — derived, again, from the Base Runs method — and the team’s actual winning percentage, predicted win percentage and estimated win percentage. The difference between the three are explained on that page itself.

K% – Percentage of plate appearances a player strikes out inBB% – Percentage of plate appearances a player walks OR draws a hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’ERA – Earned Runs Allowed Per Nine InningspERA – An ERA replicate via the Base Runs method that is adjusted for park

K% – Percentage of plate appearances a player strikes out inBB% – Percentage of plate appearances a player walks OR draws a hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
Iso* – Park-adjusted Isolated Power, Slugging Percentage minus Batting Average

Disclaimer:

This site is in no way affiliated with the Big Ten Conference, Michigan State University, or the NCAA. All opinions expressed are my own and my data is content either created myself or by others that I'm using with permission. If you have any comments, questions, suggestions or inquiries, please email me at mikerogers04 [at] gmail [dot] com.